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Models of Everywhere Revisited Models of everywhere revisited: a technological perspective BLAIR, Gordon, BEVEN, Keith, LAMB, Rob, BASSETT, Richard, CAUWENBERGHS, Kris, HANKIN, Barry, DEAN, Graham, HUNTER, Neil, EDWARD, Liz, NUNDLLOL, Vatsala, SAMREEN, Faiza <http://orcid.org/0000- 0002-9522-0713>, SIMM, Will and TOWE, Ross Available from Sheffield Hallam University Research Archive (SHURA) at: http://shura.shu.ac.uk/26223/ This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published version BLAIR, Gordon, BEVEN, Keith, LAMB, Rob, BASSETT, Richard, CAUWENBERGHS, Kris, HANKIN, Barry, DEAN, Graham, HUNTER, Neil, EDWARD, Liz, NUNDLLOL, Vatsala, SAMREEN, Faiza, SIMM, Will and TOWE, Ross (2019). Models of everywhere revisited: a technological perspective. Environmental Modelling and Software, 122, p. 104521. Copyright and re-use policy See http://shura.shu.ac.uk/information.html Sheffield Hallam University Research Archive http://shura.shu.ac.uk Environmental Modelling and Software 122 (2019) 104521 Contents lists available at ScienceDirect Environmental Modelling and Software journal homepage: http://www.elsevier.com/locate/envsoft Models of everywhere revisited: A technological perspective Gordon S. Blair a,*, Keith Beven b, Rob Lamb c,b, Richard Bassett a, Kris Cauwenberghs d, Barry Hankin e,b, Graham Dean a, Neil Hunter e, Liz Edwards a, Vatsala Nundloll a, Faiza Samreen a, Will Simm a, Ross Towe a a Ensemble Team, Lancaster University, UK b Lancaster Environment Centre, Lancaster, UK c JBA Trust, UK d Flanders Environment Agency (VMM), Belgium e JBA Consulting, UK ARTICLE INFO The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the Keywords: environmental science of a place, changing the nature of the underlying modelling process, from one in which Environmental modelling general model structures are used to one in which modelling becomes a learning process about specificplaces, in Models of everywhere particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another Grid computing it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of every­ Cloud computing where, models of everything and models at all times, being constantly re-evaluated against the most current Data science evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and non- linearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment. 1. Introduction potential generality of the approach.) This is a compelling argument, and a reaction against the view that The concept of ‘models of everywhere’ was introduced by Beven in there can be generic environmental models capable of representing 2007 (Beven, 2007), and revised in a follow up paper (Beven and Alcock, processes and behaviours across multiple places and indeed across 2012). The concept is fundamentally about having a stronger association multiple scales. Such general models are “expensive to develop, difficult between a given environmental model and the place that it represents. In to maintain and to apply because of their data demands and need for the 2012 paper, they argue that it is “useful, and even necessary, to think parameter estimation or calibration” (Beven, 2007). They also have in terms of models of everywhere … [and this] … will change the nature problems in dealing with local epistemic uncertainties and of the modelling process, from one in which general model structures are non-stationarities, for example caused by change in local characteristics used in particular catchment applications to one in which modelling and climate drivers (e.g. Prudhomme et al., 2010; Beven, 2009, 2016). becomes a learning process about places”. The ‘necessity’ stems from the Some examples of models of everywhere have been deployed but for need to constrain uncertainty in the modelling process in order to sup­ relatively constrained applications and at specific scales. However, the port policy setting and decision-making, particularly around water concept has not been developed to the extent that the authors envisaged, management (e.g. flooding and water quality), although the principles where everywhere is represented across all scales in a coherent way. can also potentially apply to other areas of environmental modelling and This paper re-examines the concept of models of everywhere from a management. (In the rest of the paper, we will tend to use examples and technological perspective arguing that, at the time, the underlying illustrations from hydrology and floodmodelling although we stress this technology was not sufficiently advanced to support the concept. Now, * Corresponding author. E-mail address: [email protected] (G.S. Blair). https://doi.org/10.1016/j.envsoft.2019.104521 Received 27 April 2018; Received in revised form 6 August 2019; Accepted 24 September 2019 Available online 27 September 2019 1364-8152/© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). G.S. Blair et al. Environmental Modelling and Software 122 (2019) 104521 however, this has changed, with significantdevelopments in areas such scenarios. However, the outputs from such models will be associated as data acquisition techniques, data storage and processing technolo- with significant uncertainty, even after calibration on historical data. gies, and data analytics capabilities, alongside a move towards a more Thus, this is a prime example where the concept of models of every- open science supported by these developments. where and the local constraint of uncertainty using local information Note that in developing ideas of models of everywhere we mean would be useful, especially when assessing the uncertainty in potential something quite different to the "hyperresolution" models that are outcomes might make a difference to the decision that might be made. A starting to be used in Earth Systems Science (e.g. Wood et al., 2011; re-evaluation of the concept of models of everywhere is therefore very Beven and Cloke, 2012; Bierkens et al., 2015; Gilbert and Maxwell, timely. 2017). With resolutions of the order of 1 km, the latter do not (as yet) The paper is structured as follows. Section 2 examines the concept of provide simulations and visualisations at scales that local stakeholders models of everywhere in more depth, highlighting the different di- can relate to directly (see the discussion of Beven et al., 2015). This is a mensions behind this initial vision, and culminating in a set of techno- critical aspect of how the models of everywhere concept has the po- logical requirements to support models of everywhere. Section 3 looks at tential to change the way that modelling is done. Both approaches do, the technological landscape, as it existed in the period 2007–2012, however, focus attention on a requirement for scale dependent param- systematically reviewing the different technological requirements and eterisations that has proven difficult to resolve (e.g. McDonnell and concluding with an overall assessment of technology readiness at that Beven, 2014). time. Section 4 repeats this analysis, but looking at the state-of-the-art The overall aim of the paper is to determine the current feasibility of now. The paper then presents ongoing research in this area, including models of everywhere, particularly in the area of hydrological modelling, the identification of a research roadmap for the implementation of the given the state-of-the-art in underlying technology. This breaks down into concept of models of everywhere, (Section 5). Section 6 documents the following objectives: related work, including existing deployments of the concept of models of everywhere. Finally, Section 7 concludes the paper with some final re- 1. To carry out a detailed examination of the concept of models of flections on models of everywhere from a technological perspective. everywhere to determine key underlying technological requirements; 2. Models of everywhere unpicked 2. To compare the state-of-the-art in technology in the period 2007–2012 and the present day to evaluate whether the time is now While models of everywhere at one level is quite a straightforward right for a widespread deployment of models of everywhere; concept representing as association of models with particular places, at 3. To provide a research roadmap to support such deployment in terms another level, it is a rich multi-dimensional conceptual framework. In of outstanding research questions and challenges. particular, we discuss three (mutually supportive) dimensions with the goal of highlighting the technological requirements to support the Note that there are other issues related to models of everywhere that overall vision: also should be addressed, most notably human and societal issues. Such issues include the need to move towards open science and open data, Models of everywhere; and the role of communities in improving models in representing local Models of everything; places. These are alluded to in the paper but a full treatment of this Models at all times. important dimension is beyond the scope of the paper. We elect instead to focus on technological readiness. These are discussed in turn below. The work is being carried out in the context of a significant re- evaluation of approaches to flood modelling and associated risk man- 2.1. Models of everywhere agement.
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